Mixture densities for video objects recognition

Hammond, Robert; Mohr, Roger

doi:10.1109/icpr.2000.906020

Cited by 17 publications

(11 citation statements)

References 7 publications

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“…A possible major improvement on the performance of the scheme could be obtained by adding intensity or color related descriptors to the measurements, and modelling their temporal evolution, as for instance described in ref. [11].…”

Section: Resultsmentioning

confidence: 95%

Recovery of the trajectories of multiple moving objects in an image sequence with a PMHT approach

Gelgon

Bouthémy

Cadre

2005

Image and Vision Computing

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This paper is concerned with the tracking of multiple moving objects in an image sequence and the reconstruction of the entire trajectories of these objects all over the sequence. More specifically, we address the joint issue of trajectory estimation and measurement-to-trajectory associations, which is the key problem in that context due to the occurrence of object occlusions or crossings. An original and efficient scheme is proposed, that adapts the probabilistic multiple hypothesis tracking (PMHT) technique to the case of tracking of regions in video, for which geometry and motion models can be introduced. Moreover, reliable partial associations can be obtained as an initialization. Data association and trajectory estimation are conducted within a probabilistic framework. The latter relies on Kalman filtering, while the former is solved with an EM algorithm for which a suitable initial configuration can be defined. The proposed tracking method is validated by experiments carried out on real image sequences depicting complex situations. q

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Section: Resultsmentioning

confidence: 95%

Recovery of the trajectories of multiple moving objects in an image sequence with a PMHT approach

Gelgon

Bouthémy

Cadre

2005

Image and Vision Computing

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“…That is, feature-based methods rely on fitting the image features to the model while appearance and deformable template-based methods strive to fit the model to the image. In general appearance models detect and track eyes based on the photometry of the eye region using fixed templates [16,39], probabilistic principal components [25] or more advanced learningbased techniques such as support vectors and Gaussian mixtures [17,19,53,61]. Feature-based methods extract particular features such as skin-color, color distribution of the eye region.…”

Section: Related Workmentioning

confidence: 99%

An improved likelihood model for eye tracking

Hansen

Hammoud

2007

Computer Vision and Image Understanding

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“…Descent results have been published recently on recognition of object classes under scales changes, occlusions and appearance changes, for a variety of applications [12] [5] [9] [6]. However, most of available public object databases used in evaluation of recognition and classifications methods are composed of synthetic objects, non-deformable objects, wellcontrolled lighting changes (mostly global illuminations), high-resolution/contrast images, and/or collected using visible sensors.…”

Section: Introductionmentioning

confidence: 99%

Disparity Based Image Segmentation For Occupant Classification

Kong

Sun

Bauson

et al.

2004 Conference on Computer Vision and Pattern Recognition Workshop

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Frontal "depowered" air bag systems are underdesign today to be even more effective than current air bags in saving lives, while at the same time reducing the potential of causing an air bag induced serious injury or death. Stereovision real-time occupant sensing systems (airbag suppression) have been developed at Delphi Automotive Systems 1 for use in comercial vehicle applications. One of the issues in such a system is that the irrelevant non-stationary background information within the field of view of the cameras results in less robust occupant classifications. In this paper, a disparity-based image segmentation method is provided. The input images are first segmented according to a pre-determined disparity threshold map and the real-time disparities of the occupants. Binary image processing techniques are used to reject noise introduced into the segmented images through low-resolution disparity calculations. The segmented images are then used for image feature extraction for a neural network classifier. For comparison, two neural network classifiers were created with and without the infrared image segmentation. Our experiements on segemnted images shown an increase of the classifier performance by at least 23% on a large database of IR images collected in extreme outdoor conditions.

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Mixture densities for video objects recognition

Abstract: The appearance of non-rigid objects detected

Cited by 17 publications

References 7 publications

Recovery of the trajectories of multiple moving objects in an image sequence with a PMHT approach

Recovery of the trajectories of multiple moving objects in an image sequence with a PMHT approach

An improved likelihood model for eye tracking

Disparity Based Image Segmentation For Occupant Classification

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